Distinct Features of Psychosocial Distress of Adolescents and Young Adults with Cancer Compared to Adults at Diagnosis: Patient-Reported Domains of Concern
Bibliographic record
Abstract
Purpose: Adolescents and young adults (AYA) (18–40) are a population of patients with cancer, who have distinctive developmental and psychosocial pressures. Using validated distress screening tools, we investigated psychosocial needs of AYA compared to older adults with cancer at diagnosis. Methods: AYA and older adult patients from British Columbia, Canada, between 2011 and 2016, who completed the Canadian Problem Checklist (CPC) and the PsychoSocial Screen for Cancer-Revised (PSSCAN-R) within 6 months of their cancer diagnosis were included in the study. Emotional, informational, physical, practical, social, and spiritual domain concerns are identified using the CPC. Psychosocial needs and distress are evaluated using the PSSCAN-R. Baseline demographics were obtained from the cancer registry. Based on gender, primary tumor site, and presence of metastasis, a 3:1 case match was performed with older adults (>40 years old). Statistical analyses included Chi square and Fisher's exact tests. Results: Two thousand and forty five AYA were case matched with 6050 older adults. Majority of patients were female (61.9%), and at diagnosis, 12.1% had metastatic disease. Top three tumor types were breast (20.4%), lymphoma (11.5%), and gastrointestinal (10.8%). The top five concerns for AYA (% AYA, % adults) were fear/worry (56.6, 42.9), understanding of illness (47.6, 41.4), sleep (35.2, 28.9), sadness (34.1, 20.0), and finances (33.8, 15.0). AYA reported higher symptoms of anxiety at baseline (% AYA, % older adults), both moderate (26.0, 19.9) and severe (26.6, 17.1) p < 0.01. Conclusion: Significant differences in psychosocial needs for AYA were seen at diagnosis across multiple domains, specifically, higher emotional, informational, physical, and financial distress. Development of supportive programming geared toward these domains early at diagnosis could benefit this distinct population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".